Civil and Environmental Engineering electronic theses and dissertations (MU)The electronic theses and dissertations of the Department of Civil and Environmental Engineering.https://hdl.handle.net/10355/52572024-03-19T03:21:23Z2024-03-19T03:21:23ZAdobe materials for structural wallsBade, Scott Charleshttps://hdl.handle.net/10355/457842022-09-29T20:35:21Z2014-01-01T00:00:00ZAdobe materials for structural walls
Bade, Scott Charles
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.]
2014-01-01T00:00:00ZAdsorption interactions of UV-aged microplastic particles with sulfamethazineHarmon, Ianhttps://hdl.handle.net/10355/915092022-09-28T16:54:56Z2022-01-01T00:00:00ZAdsorption interactions of UV-aged microplastic particles with sulfamethazine
Harmon, Ian
Due to growing plastic demand and production without proper waste management, microplastics have become common around the world throughout diverse systems. At the same time, increased detection of antibiotics in the environment has led to concerns over the spread of these antibiotics and antibiotic resistance. Evidence for microplastics' ability to adsorb and carry environmental contaminants has led to concern over the intersection of these two pollutants. Microplastic PET and recycled PET particles were UV-aged before they were used in batch adsorption tests with the antibiotic sulfamethazine. Neither the pristine condition nor the aged microplastic PET exhibit signs of adsorption with the sulfamethazine solution. Some interference was measured that may explain why adsorption was not detectable in these samples which bring up questions of leaching or desorbing compounds from microplastics that require further study.
2022-01-01T00:00:00ZAdvancements in evaluating reliability of nondestructive technologies for the detection of subsurface fracture damage in R.C. bridge decksSultan, Ali Abedhttps://hdl.handle.net/10355/654422023-04-20T15:20:50Z2017-01-01T00:00:00ZAdvancements in evaluating reliability of nondestructive technologies for the detection of subsurface fracture damage in R.C. bridge decks
Sultan, Ali Abed
During the last few decades, many efforts have been made to assess the reliability of nondestructive evaluation (NDE) technologies used for the detection of subsurface damage in concrete bridge decks. During these efforts, reliability of NDE technologies has either been described anecdotally, or been solely relegated to the probability of detection (POD) or accuracy estimation. Although these indices are important, most of the previous work did not take into account the probability of false alarm (POFA) of NDE technologies, nor did they investigate the reliability considering multiple threshold settings throughout test results. In addition, the existing body of research has used a limited physical sampling such as coring to validate NDE results. Consequently, the assessments were rather controversial, and there was no general agreement about the reliability of such technologies. Because most diagnosis systems are characterized by noisy data and less than perfect detection characteristics, reliability is to be carefully assessed considering all possible diagnosis output with multiple threshold settings within practical range of applications. In other words, when NDE data do not fall into either of the two obviously defined categories: true positive (TP), meaning the NDE data indicates a defect and there is a defect, or true negative (TN), meaning the NDT data indicates no defect and there is no defect, reliability analysis should also include the two types of incorrect indications: failure to give a positive indication in the presence of a defect (false negative, FN) and giving a positive indication when there is no defect (a false alarm or false positive, FP). The \three decades of NDI reliability assessments" report developed by Karta Technologies, Inc. in 2000 under supervision of the Air Force NDI Office stated that POD alone cannot describe the reliability of NDE technologies unless the probability of false alarm (POFA) is also considered in the analysis. POFA may be induced by noise with several possible sources: human, nature of phenomenon to be measured, and environmental conditions. The report covered nearly 150 reports and manuscripts from over 100 authors. However, a review of research literature reveals that little theoretical work on the reliability assessment in terms of both POD and POFA has been undertaken since then. In this research, the reliability of impact echo (IE), infrared thermography (IRT), and ground penetration radar (GPR) technologies for the detecting of subsurface damage in concrete plate-like members is assessed by using a statistical analysis method called receiver operating characteristic (ROC). The proposed analysis method has the capability to integrate POD and POFA indices over a wide range of decision threshold settings in a single curve, which is useful in assessing trade-off in choosing a threshold and for quantitatively comparing the performance of NDE technologies. This methodology for assessing NDE reliability is intended to provide a more effective means of comparing different technologies used in civil engineering applications, to make the evaluation process of a quantitative scheme, to reduce subjectivity and variability in interpreting NDE data, and to improve sensitivity to extract more information from NDE data. Area under ROC curve (AUC), which is interpreted as the probability of correctly classifying an arbitrarily pair of negative and positive test points, can provide for the desired quantitative reliability index, which can be used to compare the performance of one NDE technology to another. Results of this research obtained from ROC analysis indicate a great ability of IE and IR in detecting subsurface fracture damage such as delamination and debonding. In both technologies, there exist some threshold settings that can provide for a relatively high POD with very low POFA, and consequently, the areas under their ROC curves were very high. Data obtained from GPR testing, on the other hand, indicates that GPR technology has a very limited ability to detect physical damage such as subsurface delamination. This conclusion contrasts with that been argued by a large body of the previous work. However, GPR showed a good sensitivity to the presence of corrosive environments such as moisture and chloride when the concentrations of these factors are above some threshold values that may facilitate the initiation of steel reinforcement corrosion.
Field of study: Civil engineering.; Dr. Glenn Washer, Dissertation Supervisor.; Includes vita.; "July 2017."
2017-01-01T00:00:00ZAI-based framework for automatically extracting high-low features from NDS data to understand driver behaviorAboah, Armstronghttps://hdl.handle.net/10355/942052023-02-27T19:23:14Z2022-01-01T00:00:00ZAI-based framework for automatically extracting high-low features from NDS data to understand driver behavior
Aboah, Armstrong
Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.
2022-01-01T00:00:00Z